The Amplification Paradox in Recommender Systems

TL;DR

This paper investigates why recommender systems, despite favoring extreme content, do not primarily drive its consumption, by modeling user utility and content nicheness to explain the amplification paradox.

Contribution

It introduces a simple agent-based model demonstrating how user utility and content niche characteristics explain the amplification paradox in recommender systems.

Findings

Recommender systems tend to deamplify niche, extreme content due to low user utility.

The collaborative-filtering nature and niche content characteristics explain the paradox.

Modeling user utility is crucial for understanding algorithmic amplification.

Abstract

Automated audits of recommender systems found that blindly following recommendations leads users to increasingly partisan, conspiratorial, or false content. At the same time, studies using real user traces suggest that recommender systems are not the primary driver of attention toward extreme content; on the contrary, such content is mostly reached through other means, e.g., other websites. In this paper, we explain the following apparent paradox: if the recommendation algorithm favors extreme content, why is it not driving its consumption? With a simple agent-based model where users attribute different utilities to items in the recommender system, we show through simulations that the collaborative-filtering nature of recommender systems and the nicheness of extreme content can resolve the apparent paradox: although blindly following recommendations would indeed lead users to niche content, users rarely consume niche content when given the option because it is of low utility to them, which can lead the recommender system to deamplify such content. Our results call for a nuanced interpretation of ``algorithmic amplification'' and highlight the importance of modeling the utility of content to users when auditing recommender systems. Code available: https://github.com/epfl-dlab/amplification_paradox.